code: def inception_v3(
      pretrained=False,
      ctx=cpu(),
      root="~/.mxnet/models",
      partial_bn=False,
      **kwargs,
  ):
      r"""Inception v3 model from
      `"Rethinking the Inception Architecture for Computer Vision"
      <http://arxiv.org/abs/1512.00567>`_ paper.
  
      Parameters
      ----------
      pretrained : bool or str
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default $MXNET_HOME/models
          Location for keeping the model parameters.
      partial_bn : bool, default False
          Freeze all batch normalization layers during training except the first layer.
      norm_layer : object
          Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
          Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
      norm_kwargs : dict
          Additional `norm_layer` arguments, for example `num_devices=4`
          for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
      """
  
      net = Inception3(**kwargs)
      if pretrained:
          from .model_store import get_model_file
  
          net.load_parameters(
              get_model_file("inceptionv3", tag=pretrained, root=root),
              ctx=ctx,
          )
          from ..data import ImageNet1kAttr
  
          attrib = ImageNet1kAttr()
          net.synset = attrib.synset
          net.classes = attrib.classes
          net.classes_long = attrib.classes_long
      return net
  
file_path: gluon-cv/gluoncv/model_zoo/inception.py
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.contrib.nn import HybridConcurrent
label_desc: Use image node_id 9 for calling a global function with example usage: inception_v3(pretrained, ctx, root, partial_bn) and returns: net